Planning
and control problems that involve sensing uncertainty naturally live in an
information space.The concept arose in the
context of game theory and stochastic control, and has appeared as belief
spaces in AI literature (POMDPs).Just
as configuration spaces have been important for unifying virtually all path
planning problems and approaches, information spaces can serve the same purpose
for problems that involve sensing uncertainty.This talk will present a unified perspective on information spaces.Following this, many examples will be drawn
from our work, and the works of others, to illustrate the power and importance
of reasoning in terms of information spaces.The key to numerous successful approaches has been to characterize and
simplify the information space in some way. Examples include sensorless
manipulation, the Kalman filter, pursuit-evasion, and on-line navigation.

Speaker Biography

Steven M. LaValle is an Associate Professor in the
Dept. of Computer Science at the University of Illinois at Urbana-Champaign.He received
his Ph.D. in Electrical Engineering in 1995 from the University of Illinois.From 1995-1997, he was a
post-doctoral researcher and lecturer in the Computer Science Dept. at
Stanford.From 1997-2001, he was an
Assistant Professor at Iowa
State University.In 1999 he received
the CAREER award from the National Science Foundation.He has published in the areas of robotics,
computational geometry, artificial intelligence, computational biology,
computer vision, and control theory.